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UNIVERSITY PETRA MALAYSIA HYBRID RECLASSIFICATION TECHNIQUE ARTIFICIAL NEURAL NETWORK FOR LIGHTNING SEVERITY CLASSIFICATIONMUHAMMAD ALTAR MARK 2014 133PM U T HPYRIGHYBRID RECLASSIFICATION TECHNIQUE

How to fill out artificial neural network model

How to fill out artificial neural network model

1. Step 1: Prepare your dataset by cleaning and preprocessing the data.
2. Step 2: Split your dataset into training and testing sets.
3. Step 3: Initialize the artificial neural network model with the desired number of layers and neurons.
4. Step 4: Define the activation function for each layer.
5. Step 5: Randomly initialize the weights for each neuron in the network.
6. Step 6: Train the model using an optimization algorithm such as gradient descent.
7. Step 7: Evaluate the performance of the model on the testing set.
8. Step 8: Adjust the hyperparameters and repeat steps 5-7 until the desired performance is achieved.

Who needs artificial neural network model?

1. Artificial neural network models are used by various professionals and industries such as:
2. - Data scientists and machine learning researchers to develop predictive models in various domains.
3. - Financial institutions to predict stock market trends and perform risk analysis.
4. - Healthcare organizations to diagnose diseases and develop personalized treatment plans.
5. - Marketing teams to analyze customer behavior and target specific audiences with personalized advertisements.
6. - Autonomous vehicle manufacturers to develop self-driving car algorithms.
7. - Natural language processing systems to understand and generate human-like language.
8. - Gaming industry to create intelligent virtual characters and improve gameplay experience.
9. - Robotics and automation industry to control complex robotic systems.
10. - Manufacturing industry to optimize production processes and reduce costs and wastage.
11. - Energy sector to optimize energy usage and predict energy consumption patterns.
12. - Internet of Things (IoT) applications to process large amounts of sensor data and make real-time decisions.

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What is artificial neural network model?

An artificial neural network model is a computational model inspired by the way biological neural networks in the human brain process information. It consists of interconnected nodes (neurons) that work together to recognize patterns, make predictions, or classify data.

How to fill out artificial neural network model?

To fill out an artificial neural network model, one must define the architecture (layers, activation functions), optimize parameters (weights, biases), and train the model using a dataset. Documentation should also include the model's purpose, dataset used, and performance metrics.

What is the purpose of artificial neural network model?

The purpose of an artificial neural network model is to automate decision-making processes, recognize complex patterns in data, and improve predictive analytics across various domains such as healthcare, finance, and marketing.

What information must be reported on artificial neural network model?

Information that must be reported on an artificial neural network model includes model architecture, training data used, performance metrics (accuracy, precision, recall), any pre-processing steps, and the intended application of the model.